Articles | Volume 24, issue 8
https://doi.org/10.5194/hess-24-3967-2020
https://doi.org/10.5194/hess-24-3967-2020
Research article
 | 
12 Aug 2020
Research article |  | 12 Aug 2020

Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach

Manuela I. Brunner and Eric Gilleland

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Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: Catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sc., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Blum, A. G., Archfield, S. A., and Vogel, R. M.: On the probability distribution of daily streamflow in the United States, Hydrol. Earth Syst. Sci., 21, 3093–3103, https://doi.org/10.5194/hess-21-3093-2017, 2017. a, b
Bracken, C., Rajagopalan, B., Cheng, L., Kleiber, W., and Gangopadhyay, S.: Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain, Water Resour. Res., 52, 6643–6655, https://doi.org/10.1111/j.1752-1688.1969.tb04897.x, 2016. a
Breakspear, M., Brammer, M., and Robinson, P. A.: Construction of multivariate surrogate sets from nonlinear data using the wavelet transform, Physica D, 182, 1–22, https://doi.org/10.1016/S0167-2789(03)00136-2, 2003. a, b, c
Brunner, M. I. and Furrer, R.: PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization, available at: https://cran.r-project.org/web/packages/PRSim/index.html (last access: 28 May 2020), 2019. a, b
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Short summary
Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.